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Single-Cell Growth Rates in Photoautotrophic Populations Measured by Stable Isotope Probing and Resonance Raman Microspectrometry

Overview of attention for article published in Frontiers in Microbiology, August 2017
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  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

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Title
Single-Cell Growth Rates in Photoautotrophic Populations Measured by Stable Isotope Probing and Resonance Raman Microspectrometry
Published in
Frontiers in Microbiology, August 2017
DOI 10.3389/fmicb.2017.01449
Pubmed ID
Authors

Gordon T. Taylor, Elizabeth A. Suter, Zhuo Q. Li, Stephanie Chow, Dallyce Stinton, Tatiana Zaliznyak, Steven R. Beaupré

Abstract

A new method to measure growth rates of individual photoautotrophic cells by combining stable isotope probing (SIP) and single-cell resonance Raman microspectrometry is introduced. This report explores optimal experimental design and the theoretical underpinnings for quantitative responses of Raman spectra to cellular isotopic composition. Resonance Raman spectra of isogenic cultures of the cyanobacterium, Synechococcus sp., grown in (13)C-bicarbonate revealed linear covariance between wavenumber (cm(-1)) shifts in dominant carotenoid Raman peaks and a broad range of cellular (13)C fractional isotopic abundance. Single-cell growth rates were calculated from spectra-derived isotopic content and empirical relationships. Growth rates among any 25 cells in a sample varied considerably; mean coefficient of variation, CV, was 29 ± 3% (σ/[Formula: see text]), of which only ~2% was propagated analytical error. Instantaneous population growth rates measured independently by in vivo fluorescence also varied daily (CV ≈ 53%) and were statistically indistinguishable from single-cell growth rates at all but the lowest levels of cell labeling. SCRR censuses of mixtures prepared from Synechococcus sp. and T. pseudonana (a diatom) populations with varying (13)C-content and growth rates closely approximated predicted spectral responses and fractional labeling of cells added to the sample. This approach enables direct microspectrometric interrogation of isotopically- and phylogenetically-labeled cells and detects as little as 3% changes in cellular fractional labeling. This is the first description of a non-destructive technique to measure single-cell photoautotrophic growth rates based on Raman spectroscopy and well-constrained assumptions, while requiring few ancillary measurements.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 25%
Other 6 15%
Researcher 5 13%
Student > Master 4 10%
Student > Doctoral Student 3 8%
Other 8 20%
Unknown 4 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 20%
Environmental Science 8 20%
Biochemistry, Genetics and Molecular Biology 4 10%
Earth and Planetary Sciences 2 5%
Chemistry 2 5%
Other 9 23%
Unknown 7 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 August 2017.
All research outputs
#6,105,933
of 22,655,397 outputs
Outputs from Frontiers in Microbiology
#5,952
of 24,421 outputs
Outputs of similar age
#98,310
of 316,766 outputs
Outputs of similar age from Frontiers in Microbiology
#210
of 523 outputs
Altmetric has tracked 22,655,397 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 24,421 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 316,766 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 523 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.